In three-dimensional PET scans, scatter is one of the most significant physical effects relating to the degradation of image quality. In typical PET systems, scatter events can be as much as 30%˜50% of the total detected events in a PET scan. Image quality can be improved by correcting scatter events before or during image reconstruction.
There are several approaches for the correction of scatter events. Such approaches include a background subtraction or tail-fitting method, a convolution subtraction method, a Monte Carlo-based method, or a model-based scatter estimation (MBSE) including single scatter simulation (SSS). MBSE is a popular method used in modern PET systems and provides good scatter correction.
For a PET scan in a multi-bed position scanning system, bed positions that are adjacent to one another typically have at least 20% area overlap so as to achieve a more uniform axial sensitivity. To achieve good scatter estimation, MBSE requires the collection of scan data from bed positions that are adjacent to one another in order to estimate scatter that is out of the axial field of view (FOV).
However, extensive calculations are required when estimating scatter using the MBSE method. Even when a PET scan system has extremely high processing power with high optimization, scatter estimation suing the MBSE method can still take a long period of time because of the extensive calculations required. For example, a PET scan system for estimating scatter for typical patient data with 8 bed positions using a single 3.3 GHz CPU can take approximately 2500˜4300 seconds/bed position, or 450 minutes.
As a result, it can be beneficial to reduce the processing time necessary to acquire reliable scatter estimation for the correction of scatter data and the reconstruction of PET data to improve image quality.